There are many algorithms for unsupervised changepoint detection [1]}, [2]}.
In supervised changepoint detection, learning is often limited to grid search [3]}, [4]}, [5]}.
More sophisticated learning algorithms use linear models and non-linear decision trees that minimize convex surrogates of the label error [6]}, [7]}, [8]}, [9]}, [10]}.
ROC curves and AUC are used to evaluate prediction accuracy of learned penalty functions [11]}, [12]}.
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